Merge potential next release v0.4 (#187) Breaking Changes
* First draft of the new n-dimensional arrays + NB use case * Improves default implementation of multiple Array methods * Refactors tree methods * Adds matrix decomposition routines * Adds matrix decomposition methods to ndarray and nalgebra bindings * Refactoring + linear regression now uses array2 * Ridge & Linear regression * LBFGS optimizer & logistic regression * LBFGS optimizer & logistic regression * Changes linear methods, metrics and model selection methods to new n-dimensional arrays * Switches KNN and clustering algorithms to new n-d array layer * Refactors distance metrics * Optimizes knn and clustering methods * Refactors metrics module * Switches decomposition methods to n-dimensional arrays * Linalg refactoring - cleanup rng merge (#172) * Remove legacy DenseMatrix and BaseMatrix implementation. Port the new Number, FloatNumber and Array implementation into module structure. * Exclude AUC metrics. Needs reimplementation * Improve developers walkthrough New traits system in place at `src/numbers` and `src/linalg` Co-authored-by: Lorenzo <tunedconsulting@gmail.com> * Provide SupervisedEstimator with a constructor to avoid explicit dynamical box allocation in 'cross_validate' and 'cross_validate_predict' as required by the use of 'dyn' as per Rust 2021 * Implement getters to use as_ref() in src/neighbors * Implement getters to use as_ref() in src/naive_bayes * Implement getters to use as_ref() in src/linear * Add Clone to src/naive_bayes * Change signature for cross_validate and other model_selection functions to abide to use of dyn in Rust 2021 * Implement ndarray-bindings. Remove FloatNumber from implementations * Drop nalgebra-bindings support (as decided in conf-call to go for ndarray) * Remove benches. Benches will have their own repo at smartcore-benches * Implement SVC * Implement SVC serialization. Move search parameters in dedicated module * Implement SVR. Definitely too slow * Fix compilation issues for wasm (#202) Co-authored-by: Luis Moreno <morenol@users.noreply.github.com> * Fix tests (#203) * Port linalg/traits/stats.rs * Improve methods naming * Improve Display for DenseMatrix Co-authored-by: Montana Low <montanalow@users.noreply.github.com> Co-authored-by: VolodymyrOrlov <volodymyr.orlov@gmail.com>
This commit is contained in:
Lorenzo
+143
-68
@@ -12,7 +12,7 @@
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//!
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//! Example:
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//! ```
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//! use smartcore::linalg::naive::dense_matrix::*;
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//! use smartcore::linalg::basic::matrix::DenseMatrix;
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//! use smartcore::linear::logistic_regression::LogisticRegression;
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//! use smartcore::metrics::*;
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//!
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@@ -38,26 +38,29 @@
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//! &[6.6, 2.9, 4.6, 1.3],
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//! &[5.2, 2.7, 3.9, 1.4],
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//! ]);
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//! let y: Vec<f64> = vec![
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//! 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
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//! let y: Vec<i8> = vec![
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//! 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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//! ];
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//!
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//! let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
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//!
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//! let y_hat = lr.predict(&x).unwrap();
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//!
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//! let acc = ClassificationMetrics::accuracy().get_score(&y, &y_hat);
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//! let acc = ClassificationMetricsOrd::accuracy().get_score(&y, &y_hat);
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//! // or
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//! let acc = accuracy(&y, &y_hat);
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//! ```
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/// Accuracy score.
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pub mod accuracy;
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/// Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.
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pub mod auc;
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// TODO: reimplement AUC
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// /// Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.
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// pub mod auc;
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/// Compute the homogeneity, completeness and V-Measure scores.
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pub mod cluster_hcv;
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pub(crate) mod cluster_helpers;
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/// Multitude of distance metrics are defined here
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pub mod distance;
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/// F1 score, also known as balanced F-score or F-measure.
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pub mod f1;
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/// Mean absolute error regression loss.
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@@ -71,150 +74,222 @@ pub mod r2;
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/// Computes the recall.
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pub mod recall;
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use crate::linalg::BaseVector;
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use crate::math::num::RealNumber;
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use crate::linalg::basic::arrays::{Array1, ArrayView1};
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use crate::numbers::basenum::Number;
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use crate::numbers::floatnum::FloatNumber;
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use crate::numbers::realnum::RealNumber;
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use std::marker::PhantomData;
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/// A trait to be implemented by all metrics
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pub trait Metrics<T> {
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/// instantiate a new Metrics trait-object
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/// https://doc.rust-lang.org/error-index.html#E0038
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fn new() -> Self
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where
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Self: Sized;
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/// used to instantiate metric with a paramenter
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fn new_with(_parameter: f64) -> Self
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where
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Self: Sized;
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/// compute score realated to this metric
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fn get_score(&self, y_true: &dyn ArrayView1<T>, y_pred: &dyn ArrayView1<T>) -> f64;
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}
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/// Use these metrics to compare classification models.
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pub struct ClassificationMetrics {}
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pub struct ClassificationMetrics<T> {
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phantom: PhantomData<T>,
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}
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/// Use these metrics to compare classification models for
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/// numbers that require `Ord`.
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pub struct ClassificationMetricsOrd<T> {
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phantom: PhantomData<T>,
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}
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/// Metrics for regression models.
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pub struct RegressionMetrics {}
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pub struct RegressionMetrics<T> {
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phantom: PhantomData<T>,
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}
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/// Cluster metrics.
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pub struct ClusterMetrics {}
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impl ClassificationMetrics {
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/// Accuracy score, see [accuracy](accuracy/index.html).
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pub fn accuracy() -> accuracy::Accuracy {
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accuracy::Accuracy {}
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}
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pub struct ClusterMetrics<T> {
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phantom: PhantomData<T>,
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}
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impl<T: Number + RealNumber + FloatNumber> ClassificationMetrics<T> {
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/// Recall, see [recall](recall/index.html).
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pub fn recall() -> recall::Recall {
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recall::Recall {}
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pub fn recall() -> recall::Recall<T> {
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recall::Recall::new()
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}
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/// Precision, see [precision](precision/index.html).
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pub fn precision() -> precision::Precision {
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precision::Precision {}
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pub fn precision() -> precision::Precision<T> {
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precision::Precision::new()
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}
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/// F1 score, also known as balanced F-score or F-measure, see [F1](f1/index.html).
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pub fn f1<T: RealNumber>(beta: T) -> f1::F1<T> {
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f1::F1 { beta }
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pub fn f1(beta: f64) -> f1::F1<T> {
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f1::F1::new_with(beta)
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}
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/// Area Under the Receiver Operating Characteristic Curve (ROC AUC), see [AUC](auc/index.html).
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pub fn roc_auc_score() -> auc::AUC {
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auc::AUC {}
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// /// Area Under the Receiver Operating Characteristic Curve (ROC AUC), see [AUC](auc/index.html).
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// pub fn roc_auc_score() -> auc::AUC<T> {
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// auc::AUC::<T>::new()
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// }
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}
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impl<T: Number + Ord> ClassificationMetricsOrd<T> {
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/// Accuracy score, see [accuracy](accuracy/index.html).
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pub fn accuracy() -> accuracy::Accuracy<T> {
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accuracy::Accuracy::new()
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}
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}
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impl RegressionMetrics {
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impl<T: Number + FloatNumber> RegressionMetrics<T> {
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/// Mean squared error, see [mean squared error](mean_squared_error/index.html).
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pub fn mean_squared_error() -> mean_squared_error::MeanSquareError {
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mean_squared_error::MeanSquareError {}
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pub fn mean_squared_error() -> mean_squared_error::MeanSquareError<T> {
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mean_squared_error::MeanSquareError::new()
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}
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/// Mean absolute error, see [mean absolute error](mean_absolute_error/index.html).
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pub fn mean_absolute_error() -> mean_absolute_error::MeanAbsoluteError {
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mean_absolute_error::MeanAbsoluteError {}
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pub fn mean_absolute_error() -> mean_absolute_error::MeanAbsoluteError<T> {
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mean_absolute_error::MeanAbsoluteError::new()
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}
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/// Coefficient of determination (R2), see [R2](r2/index.html).
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pub fn r2() -> r2::R2 {
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r2::R2 {}
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pub fn r2() -> r2::R2<T> {
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r2::R2::<T>::new()
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}
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}
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impl ClusterMetrics {
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impl<T: Number + Ord> ClusterMetrics<T> {
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/// Homogeneity and completeness and V-Measure scores at once.
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pub fn hcv_score() -> cluster_hcv::HCVScore {
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cluster_hcv::HCVScore {}
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pub fn hcv_score() -> cluster_hcv::HCVScore<T> {
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cluster_hcv::HCVScore::<T>::new()
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}
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}
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/// Function that calculated accuracy score, see [accuracy](accuracy/index.html).
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/// * `y_true` - cround truth (correct) labels
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/// * `y_pred` - predicted labels, as returned by a classifier.
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pub fn accuracy<T: RealNumber, V: BaseVector<T>>(y_true: &V, y_pred: &V) -> T {
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ClassificationMetrics::accuracy().get_score(y_true, y_pred)
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pub fn accuracy<T: Number + Ord, V: ArrayView1<T>>(y_true: &V, y_pred: &V) -> f64 {
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let obj = ClassificationMetricsOrd::<T>::accuracy();
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obj.get_score(y_true, y_pred)
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}
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/// Calculated recall score, see [recall](recall/index.html)
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/// * `y_true` - cround truth (correct) labels.
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/// * `y_pred` - predicted labels, as returned by a classifier.
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pub fn recall<T: RealNumber, V: BaseVector<T>>(y_true: &V, y_pred: &V) -> T {
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ClassificationMetrics::recall().get_score(y_true, y_pred)
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pub fn recall<T: Number + RealNumber + FloatNumber, V: ArrayView1<T>>(
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y_true: &V,
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y_pred: &V,
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) -> f64 {
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let obj = ClassificationMetrics::<T>::recall();
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obj.get_score(y_true, y_pred)
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}
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/// Calculated precision score, see [precision](precision/index.html).
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/// * `y_true` - cround truth (correct) labels.
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/// * `y_pred` - predicted labels, as returned by a classifier.
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pub fn precision<T: RealNumber, V: BaseVector<T>>(y_true: &V, y_pred: &V) -> T {
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ClassificationMetrics::precision().get_score(y_true, y_pred)
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pub fn precision<T: Number + RealNumber + FloatNumber, V: ArrayView1<T>>(
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y_true: &V,
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y_pred: &V,
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) -> f64 {
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let obj = ClassificationMetrics::<T>::precision();
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obj.get_score(y_true, y_pred)
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}
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/// Computes F1 score, see [F1](f1/index.html).
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/// * `y_true` - cround truth (correct) labels.
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/// * `y_pred` - predicted labels, as returned by a classifier.
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pub fn f1<T: RealNumber, V: BaseVector<T>>(y_true: &V, y_pred: &V, beta: T) -> T {
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ClassificationMetrics::f1(beta).get_score(y_true, y_pred)
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pub fn f1<T: Number + RealNumber + FloatNumber, V: ArrayView1<T>>(
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y_true: &V,
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y_pred: &V,
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beta: f64,
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) -> f64 {
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let obj = ClassificationMetrics::<T>::f1(beta);
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obj.get_score(y_true, y_pred)
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}
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/// AUC score, see [AUC](auc/index.html).
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/// * `y_true` - cround truth (correct) labels.
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/// * `y_pred_probabilities` - probability estimates, as returned by a classifier.
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pub fn roc_auc_score<T: RealNumber, V: BaseVector<T>>(y_true: &V, y_pred_probabilities: &V) -> T {
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ClassificationMetrics::roc_auc_score().get_score(y_true, y_pred_probabilities)
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}
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// /// AUC score, see [AUC](auc/index.html).
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// /// * `y_true` - cround truth (correct) labels.
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// /// * `y_pred_probabilities` - probability estimates, as returned by a classifier.
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// pub fn roc_auc_score<T: Number + PartialOrd, V: ArrayView1<T> + Array1<T> + Array1<T>>(
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// y_true: &V,
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// y_pred_probabilities: &V,
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// ) -> T {
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// let obj = ClassificationMetrics::<T>::roc_auc_score();
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// obj.get_score(y_true, y_pred_probabilities)
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// }
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/// Computes mean squared error, see [mean squared error](mean_squared_error/index.html).
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/// * `y_true` - Ground truth (correct) target values.
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/// * `y_pred` - Estimated target values.
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pub fn mean_squared_error<T: RealNumber, V: BaseVector<T>>(y_true: &V, y_pred: &V) -> T {
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RegressionMetrics::mean_squared_error().get_score(y_true, y_pred)
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pub fn mean_squared_error<T: Number + FloatNumber, V: ArrayView1<T>>(
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y_true: &V,
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y_pred: &V,
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) -> f64 {
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RegressionMetrics::<T>::mean_squared_error().get_score(y_true, y_pred)
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}
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/// Computes mean absolute error, see [mean absolute error](mean_absolute_error/index.html).
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/// * `y_true` - Ground truth (correct) target values.
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/// * `y_pred` - Estimated target values.
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pub fn mean_absolute_error<T: RealNumber, V: BaseVector<T>>(y_true: &V, y_pred: &V) -> T {
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RegressionMetrics::mean_absolute_error().get_score(y_true, y_pred)
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pub fn mean_absolute_error<T: Number + FloatNumber, V: ArrayView1<T>>(
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y_true: &V,
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y_pred: &V,
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) -> f64 {
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RegressionMetrics::<T>::mean_absolute_error().get_score(y_true, y_pred)
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}
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/// Computes R2 score, see [R2](r2/index.html).
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/// * `y_true` - Ground truth (correct) target values.
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/// * `y_pred` - Estimated target values.
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pub fn r2<T: RealNumber, V: BaseVector<T>>(y_true: &V, y_pred: &V) -> T {
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RegressionMetrics::r2().get_score(y_true, y_pred)
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pub fn r2<T: Number + FloatNumber, V: ArrayView1<T>>(y_true: &V, y_pred: &V) -> f64 {
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RegressionMetrics::<T>::r2().get_score(y_true, y_pred)
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}
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/// Homogeneity metric of a cluster labeling given a ground truth (range is between 0.0 and 1.0).
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/// A cluster result satisfies homogeneity if all of its clusters contain only data points which are members of a single class.
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/// * `labels_true` - ground truth class labels to be used as a reference.
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/// * `labels_pred` - cluster labels to evaluate.
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pub fn homogeneity_score<T: RealNumber, V: BaseVector<T>>(labels_true: &V, labels_pred: &V) -> T {
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ClusterMetrics::hcv_score()
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.get_score(labels_true, labels_pred)
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.0
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pub fn homogeneity_score<
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T: Number + FloatNumber + RealNumber + Ord,
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V: ArrayView1<T> + Array1<T>,
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>(
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y_true: &V,
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y_pred: &V,
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) -> f64 {
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let mut obj = ClusterMetrics::<T>::hcv_score();
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obj.compute(y_true, y_pred);
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obj.homogeneity().unwrap()
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}
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///
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/// Completeness metric of a cluster labeling given a ground truth (range is between 0.0 and 1.0).
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/// * `labels_true` - ground truth class labels to be used as a reference.
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/// * `labels_pred` - cluster labels to evaluate.
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pub fn completeness_score<T: RealNumber, V: BaseVector<T>>(labels_true: &V, labels_pred: &V) -> T {
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ClusterMetrics::hcv_score()
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.get_score(labels_true, labels_pred)
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.1
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pub fn completeness_score<
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T: Number + FloatNumber + RealNumber + Ord,
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V: ArrayView1<T> + Array1<T>,
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>(
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y_true: &V,
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y_pred: &V,
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) -> f64 {
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let mut obj = ClusterMetrics::<T>::hcv_score();
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obj.compute(y_true, y_pred);
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obj.completeness().unwrap()
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}
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/// The harmonic mean between homogeneity and completeness.
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/// * `labels_true` - ground truth class labels to be used as a reference.
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/// * `labels_pred` - cluster labels to evaluate.
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pub fn v_measure_score<T: RealNumber, V: BaseVector<T>>(labels_true: &V, labels_pred: &V) -> T {
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ClusterMetrics::hcv_score()
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.get_score(labels_true, labels_pred)
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.2
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pub fn v_measure_score<T: Number + FloatNumber + RealNumber + Ord, V: ArrayView1<T> + Array1<T>>(
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y_true: &V,
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y_pred: &V,
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) -> f64 {
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let mut obj = ClusterMetrics::<T>::hcv_score();
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obj.compute(y_true, y_pred);
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obj.v_measure().unwrap()
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}
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Reference in New Issue
Block a user